Recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, includin...
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Sarcasm detection in social media is a challenging task due to its inherent reliance on contextual cues, tone, and cultural nuances. In recent years, multi-model deep learning frameworks have emerged as a powerful app...
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ISBN:
(纸本)9798350355611
Sarcasm detection in social media is a challenging task due to its inherent reliance on contextual cues, tone, and cultural nuances. In recent years, multi-model deep learning frameworks have emerged as a powerful approach for addressing these challenges, particularly in regional social media, where language variations and local idiomatic expressions complicate the detection process. This survey explores the latest developments in multi-model deep learning frameworks for sarcasm detection, focusing on their application in regional social media. The survey begins by reviewing foundational techniques in sarcasm detection, including traditional machine learning approaches that rely on handcrafted features. These methods, although effective in certain contexts, often fail to capture the subtleties of sarcasm in informal, region-specific languages. The advent of deep learning has led to significant advancements, particularly through models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. These architectures, combined with Natural Language Processing (NLP) techniques, have enhanced the ability to identify sarcasm through text analysis. However, single-modal approaches focusing solely on text fail to fully capture sarcasm's multimodal nature, especially on platforms where users often express themselves through a combination of text, images, emojis, and video. This has led to the development of multi-model frameworks that integrate various data modalities, such as text, image, and user behaviour, to better understand the context of sarcastic expressions. In regional social media, where local language and cultural symbols play a crucial role, these multi-model approaches prove even more valuable. This survey highlights key multi-model frameworks, emphasizing their use in regional settings. By examining datasets, model architectures, and evaluation metrics, the survey underscores the importance of combining textual and non-textual
Data analytics are difficult with unstructured data since it lacks pre-formatting, which makes up a large amount of information on social media. Research has looked into the potential of machine learning and natural l...
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Brain tumor classification & segmentation are crucial challenges in medical image analysis. Brain tumors are often incurable malignancies that arise from glial support cells. Medical study reveals that manual clas...
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In the contemporary landscape of online social networks, preserving users' privacy while applying clustering techniques is a pivotal concern. This paper explores the integration of differential privacy into social...
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Brain tumor detection is an essential undertaking in the fields of neuroimaging and medical image analysis. It detects the abnormal growth of tissues within MRI scans. In the past, manual detection was performed, whic...
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This study explores the use of Graph Neural Networks (GNNs) to classify enzyme functions using the ENZYMES dataset, which represents proteins and their interactions as graphs. The literature review highlights the evol...
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In a machine learning classification problem, feature selection is a required pre-processing phase which identifies important and relevant features from the dataset to potentially reduce the computational complexity a...
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Today, security threats provide the greatest challenge to sensor networks. WSNs, or wireless sensor networks, are commonly implemented in practical settings. However, WSNs are subject to a wide variety of assaults fro...
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ISBN:
(纸本)9781665475129
Today, security threats provide the greatest challenge to sensor networks. WSNs, or wireless sensor networks, are commonly implemented in practical settings. However, WSNs are subject to a wide variety of assaults from both inside and outside the network, with insider attacks being especially difficult to detect and defend against. Most often, the clustered WSNs are at risk from an insider threat, in which the hackers pick and choose which incoming data packets to discard. Unmonitored network clusters are to blame for this predicament. Selective forwarding attacks (SFA) in WSN have the potential to cause damage to a wide variety of mission critical applications, including military surveillance and forest fire censorship. In these types of attacks, malicious nodes function like regular nodes for the majority of the time, but they may occasionally drop sensitive packets selectively, such as a packet recording the activity of a dissimilar power, which makes it more difficult to determine whether or not they have malicious intent. The focus of this research is the protective method against Selective forwarding assaults in WSN. First, this paper provides an overview of Wireless sensor networks, including their essential parts, defining features, and potential uses. Also, WSNs are becoming increasingly popular, but their architecture needs to be thoroughly comprehended before they can be used in a practical setting. This work uses the Open System Interconnection (OSI) model and several protocols to analyze WSN architecture, laying a solid groundwork for wireless sensor networks and helping readers find a convenient summary of key concepts, protocols, and challenges on the path to an appropriate design model for WSNs. The use of WSNs has expanded to encompass many different fields and functions. These networks are typically designed for a specific purpose, making it difficult to adapt them to meet the needs of emerging technologies. As a result, more and more WSNs will lik
Moving around in their surroundings is challenging for elderly people and people with visual impairments. The majority of functional sticks in use today are not intelligent and lack an innate ability to recognize obst...
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